SynthesisBMC neurology2025
Convolutional neural network models of structural MRI for discriminating categories of cognitive impairment: a systematic review and meta-analysis.
Synthesis in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept.Scientific reports · 2026Article
- Resolving Heterogeneity in the Diagnosis of Alzheimer's Disease and its Progression Using Multimodal Data.Journal of molecular neuroscience : MN · 2026Article
- SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.Frontiers in neuroimaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundAlzheimer's disease (AD) and mild cognitive impairment (MCI) pose significant challenges to public health and underscore the need for accurate and early diagnostic tools. Structural magnetic resonance imaging (sMRI) combined with advanced analytical techniques like convolutional neural networks (CNNs) seemed to offer a promising avenue for the diagnosis of these conditions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of CNN algorithms applied to sMRI data in differentiating between AD, MCI, and normal cognition (NC).
methodsFollowing the PRISMA-DTA guidelines, a comprehensive literature search was carried out in PubMed and Web of Science databases for studies published between 2018 and 2024. Studies were included if they employed CNNs for the diagnostic classification of sMRI data from participants with AD, MCI, or NC. The methodological quality of the included studies was assessed using the QUADAS-2 and METRICS tools. Data extraction and statistical analysis were performed to calculate pooled diagnostic accuracy metrics.
resultsA total of 21 studies were included in the study, comprising 16,139 participants in the analysis. The pooled sensitivity and specificity of CNN algorithms for differentiating AD from NC were 0.92 and 0.91, respectively. For distinguishing MCI from NC, the pooled sensitivity and specificity were 0.74 and 0.79, respectively. The algorithms also showed a moderate ability to differentiate AD from MCI, with a pooled sensitivity and specificity of 0.73 and 0.79, respectively. In the pMCI versus sMCI classification, a pooled sensitivity was 0.69 and a specificity was 0.81. Heterogeneity across studies was significant, as indicated by meta-regression results.
conclusionCNN algorithms demonstrated promising diagnostic performance in differentiating AD, MCI, and NC using sMRI data. The highest accuracy was observed in distinguishing AD from NC and the lowest accuracy observed in distinguishing pMCI from sMCI. These findings suggest that CNN-based radiomics has the potential to serve as a valuable tool in the diagnostic armamentarium for neurodegenerative diseases. However, the heterogeneity among studies indicates a need for further methodological refinement and validation.
trial registrationThis systematic review was registered in PROSPERO (Registration ID: CRD42022295408).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.